About ConnectomeDB

Before using this resource, please read our Terms & Conditions.

ConnectomeDB is a comprehensive and ongoing project that provides a high-quality database of interacting ligand-receptor pairs and analysis tools. Launched in 2015, it aims to enhance understanding of cell-cell communication in humans and other species, supporting biological and medical research. Developers: YCU Bioinformatics Lab & Perkins Systems Biology Genomics Lab.

ConnectomeDB2025 - Database & Online Resource for Cell-to-Cell Communication Predictions

  • 3,436 Manually Curated Human LR pairs: comprehensive and most accurate with primary literature support
  • 1,081 Ligands & 857 Receptors: metadata and functional annotations
  • Homologous Interactions: ~98% LR pairs in mouse and in 11 other vertebrate species
  • Free, user-friendly resource: developed with Quarto 1.7+ and BioRender

Publication: ConnectomeDB2025, a high quality manually curated ligand-receptor database for cell-to-cell communication prediction, P Liu, et al. (TBA)
GitHub repo: https://github.com/bioinfo-YCU/ConnectomeDB (open upon publication)

Access ConnectomeDB2025 Online

An internet connection and a web browser are required. The following major operating systems and web browsers have been tested and are quaranteed to work:

  • Windows 10/11: Google Chrome, Microsoft Edge
  • macOS 15+: Google Chrome, Safari

Not familiar with ConnectomeDB 2025 or want to learn more? Please check out our tutorials (currently under construction).

Your Feedback

We welcome your feedback and suggestions for improving ConnectomeDB content and would be glad to hear any ideas for further development.
If you would like to contribute to the ConnectomeDB project, please visit the GitHub repository (open upon publication) or email us.

Network Analysis Toolkit for Multicellular Interactions (NATMI)

  • our Python-based toolkit designed for constructing and analyzing multi-cellular communication networks in multiple species
  • predicts cell-to-cell communication at the levels of niches, tissues, and the entire organism
  • can use both single-cell and bulk gene expression and proteomic data

Publication: Predicting cell-to-cell communication networks using NATMI (Nat commun, 2020)
GitHub repo: https://github.com/forrest-lab/NATMI

Please cite the following papers if you use ConnectomeDB or NATMI in your publication:

  1. ConnectomeDB2025, a high quality manually curated ligand-receptor database for cell-to-cell communication prediction, P Liu, et al. (TBA)
    PMID: (TBA); DOI: (TBA)

  2. Predicting cell-to-cell communication networks using NATMI, R Hou, et al. Nature communications 11(1), 5011, 2020
    PMID: 33024107; DOI: 10.1038/s41467-020-18873-z

  3. A draft network of ligand–receptor-mediated multicellular signalling in human, JA Ramilowski, et al. Nature communications 6(1), 7866, 2015
    PMID: 26198319; DOI: 10.1038/ncomms8866

Let us Help You

General inquiries: send Alistair, Jordan, and Rui an email.

Online resource issues: fill out this Inquiry form or contact us via this email.